Use of <scp> O <sub>3</sub> </scp> and <scp> O <sub>3</sub> </scp> / <scp> H <sub>2</sub> O <sub>2</sub> </scp> for degradation of organic matter from Bayer liquor towards new resource management: Kinetic and mechanism
Bibliographic record
Abstract
Abstract Since the quality of bauxite resources has decreased and the organic carbon content has increased, different approaches are explored to remove the organic matter in alumina production. Advanced oxidative processes (AOPs) represent a possibility since they are widely used as an alternative for treating wastewaters to degrade organic pollutant molecules and in hydrometallurgy processes. For this reason, the goal of the project was the ozonation of Bayer liquor for organic matter removal. The ozone concentration was evaluated over time, as well as the H 2 O 2 concentration and temperature. Results showed that the total organic carbon (TOC) removal achieved 19% in the most optimized condition with a kinetic rate of 0.0157 h −1 –21.9 mg/L O 3 , 0.05 mol/L H 2 O 2 at 80°C. The colour of the liquor changed from dark brown to white‐yellow, indicating that the size of the organic compounds had decreased. Also, 95.4% of degraded TOC formed CO 2 , and almost 50% of the organic matter was oxalate compounds. The energy required for ozone production versus removed organic matter demonstrated that the technique proposed might be technically and economically feasible to be applied in the Bayer process. The study demonstrates the application of AOP in an extremely alkaline condition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".